New designs of water-cooled reactors that include thermal-hydraulics systems undergo safety analysis during the licensing process. For economic reasons and safety concerns, systems are firstly tested on reduced scale test facilities. A proper scaling ensures that dominant thermalhydraulics safety-related phenomena are captured, even though unavoidable scale distortions occur. Some existing scaling methods, based on prior knowledge of the physical phenomena at stake, can quantify such distortions. They are however limited when the phenomena are non-linear and coupled, or even not formalized as an equation. Dimensionless numbers play a central role in scaling techniques as their scale invariant properties help preserve similarities between reactor and test facilities. Building on this principle, this paper proposes a data driven alternative to traditional scaling techniques. From a dataset of physical variables describing the phenomenon of interest, the method identifies a governing law that captures the dominant safety related phenomenon. This governing law is expressed in terms of dimensionless numbers, which are physically meaningful combinations of dimensional variables and are automatically inferred by the algorithm. Based on a clear mathematical formulation, the proposed data driven method combines advanced regression analysis with the constrained optimization of a cross validation based objective function. Two variants are presented: one assuming that the output of the nondimensional governing law is known, and an extension in which this output is estimated. Both variants identify the dominant input dimensionless number as well as the explicit form of the governing law. As a proof of concept, the method is tested on a simulated dataset representative of single-phase natural circulation in a passive heat removal system.
Soosan Naderi Mighan, Jelena Mišić, Vojislav B. Mišić
Ethereum 2.0 uses a Proof-of-Stake-based consensus which aims to minimize the impact of malicious validators by decentralizing the voting protocol. In this paper we investigate the lifecycle of a validator in a consensus protocol similar to Ethereum 2.0 but with simplifications introduced for tractability. In particular, the protocol operates with near-single slot finality and includes the impact of behaviors such as truthful and false voting, abstention from voting, voluntary exit from the validator committee, and return to the committee upon depositing the required stake. Using probabilistic techniques and a Markov chain model, we examine the impact of all those factors on consensus probability. Our results indicate that the probability of truthful voting has a predominant effect on consensus, although the interplay between probabilities of voluntary exit and waiting before returning to the committee also plays an important role. We also investigate the process of fork resolution and model the behavior of the blockchain in the presence of multiple tips, and we show that probability of truthful voting is equally important in this case as higher values accelerate fork resolution.
The instability of algorithmic and hybrid stablecoins has become a systemic concern in decentralized finance. This paper proposes a unified, interpretable, and uncertainty-aware framework that integrates graph-based deep reinforcement learning, GARCH econometric modeling, and Bayesian inference. Multi-stage reinforcement learning agents simulate interactions between arbitrageurs and protocol mechanisms. GARCH models capture volatility dynamics, while Bayesian methods provide confidence intervals for peg deviation forecasts, enabling adaptive prediction and transparent risk interpretation. The framework is validated using over eight million on-chain and off-chain records across 120 scenarios involving USDT, USDC, and TerraUSD. It achieves 89 per cent crisis prediction accuracy and 83 per cent reflexivity modeling performance, significantly outperforming six benchmark models. Notably, the system issued early warnings up to 72 hours before the TerraUSD collapse. Ablation studies confirm the unique contribution of each module. In addition to technical improvements, the framework outputs a stability index and dynamic reserve recommendations to support policy response and supervisory planning. Compared to existing approaches, this is the first framework to combine dynamic simulation, interpretability, and probabilistic forecasting in a single architecture. It offers practical value for stablecoin monitoring and establishes a methodological foundation for future research in digital asset risk assessment.
The rapid development of blockchain technology has propelled the tokenization of real world assets (RWA) in a new direction in the financial industry. This paper delves into market changes, technology applications, actual cases, and regulatory policies to provide a comprehensive overview of the current development, application fields, and future prospects of RWA. The research found that RWA has already made significant attempts to implement securities, intellectual property rights, artworks, agriculture, and other fields, demonstrating broad market prospects, but there are still problems such as unclear legal frameworks and high technical thresholds to be resolved. In the future, RWA needs to focus on technological innovation, expand the market, improve supervision, strengthen cross-industry collaboration, and build a robust decentralized finance (DeFi) ecosystem. This not only provides decision-making references for relevant parties but also helps promote the development of RWA in the global financial system and the integration of traditional assets and digital assets.
Rug pulls pose a grave threat to the cryptocurrency ecosystem, leading to substantial financial loss and undermining trust in decentralized finance (DeFi) projects. With the emergence of new rug pull patterns, research on rug pull is out of state. To fill this gap, we first conducted an extensive analysis of the literature review, encompassing both scholarly and industry sources. By examining existing academic articles and industrial discussions on rug pull projects, we present a taxonomy inclusive of 34 root causes, introducing six new categories inspired by industry sources: burn, hidden owner, ownership transfer, unverified contract, external call, and fake LP lock. Based on the developed taxonomy, we evaluated current rug pull datasets and explored the effectiveness and limitations of existing detection mechanisms. Our evaluation indicates that the existing datasets, which document 2,448 instances, address only 7 of the 34 root causes, amounting to a mere 20% coverage. It indicates that existing open-source datasets need to be improved to study rug pulls. In response, we have constructed a more comprehensive dataset containing 2,360 instances, expanding the coverage to 54% with the best effort. In addition, the examination of 14 detection tools showed that they can identify 25 of the 34 root causes, achieving a coverage of 73.5%. There are nine root causes (Fake LP Lock, Hidden Fee, and Destroy Token, Fake Money Transfer, Ownership Transfer, Liquidity Pool Block, Freeze Account, Wash-Trading, Hedge) that the existing tools cannot cover. Our work indicates that there is a significant gap between current research and detection tools, and the actual situation of rug pulls.
Set reconciliation, where two parties hold fixed-length bit strings and run a protocol to learn the strings they are missing from each other, is a fundamental task in many distributed systems. We present Rateless Invertible Bloom Lookup Tables (Rateless IBLTs), the first set reconciliation protocol, to the best of our knowledge, that achieves low computation cost and near-optimal communication cost across a wide range of scenarios: set differences of one to millions, bit strings of a few bytes to megabytes, and workloads injected by potential adversaries. Rateless IBLT is based on a novel encoder that incrementally encodes the set difference into an infinite stream of coded symbols, resembling rateless error-correcting codes. We compare Rateless IBLT with state-of-the-art set reconciliation schemes and demonstrate significant improvements. Rateless IBLT achieves 3--4× lower communication cost than non-rateless schemes with similar computation cost, and 2--2000× lower computation cost than schemes with similar communication cost. We show the real-world benefits of Rateless IBLT by applying it to synchronize the state of the Ethereum blockchain, and demonstrate 5.6× lower end-to-end completion time and 4.4× lower communication cost compared to the system used in production.
Abstract To assess and improve process plants safety and integrity in real time and intervene in timely manner. Assets are safe and we can prove it by employing Real Time Data Analytics to reduce operational process safety risk and improve plant safety and integrity assurance. Real time IPF/Bypass management and validation guarantee the overall effective safety system for safe Operational Excellence. Petronas Upstream employed Data Analytical tool to facilitate analysis of Instrumented Protective Functions performance, Bypass Management, documentation of proof testing, and management of required test schedules for Process Safety. Automated reporting of analytical tool provides testing requirements for current and future protective functions throughout the facility. All Bypasses of Instrumented Safety System (IPF) are monitored in Real Time and Descriptive Analytics has been developed to provide insights to safe operation of the facility. The software analytical tool implemented enable validation testing when the system is activated. In the event of a failure or fault of the safety system, plant personnel are notified and all information updated in centralized Web Based Dashboard for high level of data transparency across all stake holders. Descriptive analytical system helps to identify potential equipment malfunction/failures in advance. Automatic Alert & Notification of anomalies are sent to identified stakeholders. There is no room for error, yet there are many ways to bypass IPF from Safety Instrumented System(SIS) so Real Time Bypass Management is a key success factor for Process Safety Assurance & Asset Integrity. Real Time Web Based Dashboard designed to customize to user specific actionable analytics to Visualize Current Risk and Current availability of Instrumented Protective functions to identify risk. Performance Reporting strengthen the Analysis and reporting of IPF performance against design criteria. Real Time Analytics resulted from IPF Design Data for Design time, process safety time, testing interval, risk, consequence, severity, SIL level and more. Real Time Analytics are available on-demand through a single web based and mobile-enabled user interface (withing domain). Real Time Analytics for multiple user level provides indispensable capabilities to guarantee effective management of IPF Performance, Bypass Management, Demand on safety System rate tracking, Excusions Management, Safety and Operational Risk Visualization. The Data Analytics is aligned to management aspiration of Going Digital for sustainable future and to cultivate effective collaboration. Additional Information Standards, such as API RP 754, IEC 61508 and 61511, ISA 18.2; govern the monitoring and maintenance of Asset's Instrumented Protective Functions including tracking and documenting availability, demand rate and failures and proof testing. Employing Real Time Analytical tool will help PETRONAS to achieve proven in use high quality equipment Reliability data for value creation. Analytical information enables assurance of process safety governance to achieve objective of -
In this paper, we propose a new method to perform Sparse Kernel Principal Component Analysis (SKPCA) and also mathematically analyze the validity of SKPCA. We formulate SKPCA as a constrained optimization problem with elastic net regularization (Hastie et al.) in kernel feature space and solve it. We consider outlier detection (where KPCA is employed) as an application for SKPCA, using the RBF kernel. We test it on 5 real-world datasets and show that by using just 4% (or even less) of the principal components (PCs), where each PC has on average less than 12% non-zero elements in the worst case among all 5 datasets, we are able to nearly match and in 3 datasets even outperform KPCA. We also compare the performance of our method with a recently proposed method for SKPCA by Wang et al. and show that our method performs better in terms of both accuracy and sparsity. We also provide a novel probabilistic proof to justify the existence of sparse solutions for KPCA using the RBF kernel. To the best of our knowledge, this is the first attempt at theoretically analyzing the validity of SKPCA.
Georgios Darivianakis, Angelos Georghiou, John Lygeros
Distributed model predictive control (MPC) has been proven a successful method in regulating the operation of large-scale networks of constrained dynamical systems. This paper is concerned with cooperative distributed MPC in which the decision actions of the systems are usually derived by the solution of a system-wide optimization problem. However, formulating and solving such large-scale optimization problems is often a hard task which requires extensive information communication among the individual systems and fails to address privacy concerns in the network. Hence, the main challenge is to design decision policies with a prescribed structure so that the resulting system-wide optimization problem to admit a loosely coupled structure and be amendable to distributed computation algorithms. In this paper, we propose a decentralized problem synthesis scheme which only requires each system to communicate sets which bound its states evolution to neighboring systems. The proposed method alleviates concerns on privacy since this limited communication scheme does not reveal the exact characteristics of the dynamics within each system. In addition, it enables a distributed computation of the solution, making our method highly scalable. We demonstrate in a number of numerical studies, inspired by engineering and finance, the efficacy of the proposed approach which leads to solutions that closely approximate those obtained by the centralized formulation only at a fraction of the computational effort.
Accurate and robust control methodologies are critical to the reliable and safe operation of engineering systems. Sliding mode control (SMC) is a form of variable structure control and is regarded as one of the most effective nonlinear robust control approaches. The control law is designed so that the system state trajectories are forced towards the sliding surface and stays within a region of it. The switching gain in the control signal brings an inherent amount of stability to the control process. However, the controller is only as effective as the knowledge of critical system states and parameters. Estimation strategies, such as the Kalman filter or the smooth variable structure filter (SVSF), may be employed to improve the quality of the state estimates used by control methods. A recently developed SVSF formulation, referred to as the second-order SVSF, offers robustness and chattering suppression properties of second-order sliding mode systems. It produces robust state estimation by preserving the first- and second-order sliding conditions such that the measurement error and its first difference are pushed towards zero. This paper aims to combine the SMC with the second-order SVSF in an effort to develop and offer an improved control strategy. It is proposed that this controller will offer an improvement in terms of controller accuracy without affecting its inherent stability and robustness. An electro hydrostatic actuator will be used for proof of concept, and future work will extend the application to automotive power trains.
Loss of control is a serious problem in aviation that primarily affects General Aviation. Technological advancements can help mitigate the problem, but the FAA certification process makes certain solutions economically unfeasible. This investigation presents the design of a generic adaptive autopilot that could potentially lead to a single certification for use in several makes and models of aircraft. The autopilot consists of a conventional controller connected in series with a robust direct adaptive model reference controller. In this architecture, the conventional controller is tuned once to provide outer-loop guidance and navigation to a reference model. The adaptive controller makes unknown aircraft behave like the reference model, allowing the conventional controller to successfully provide navigation without the need for retuning. A strong theoretical foundation is presented as an argument for the safety and stability of the controller. The stability proof of direct adaptive controllers require that the plant being controlled has no unstable transmission zeros and has a nonzero high frequency gain. Because most conventional aircraft do not readily meet these requirements, a process known as sensor blending was used. Sensor blending consists of using a linear combination of the plant’s outputs that has no unstable transmission zeros and has a nonzero high frequency gain to drive the adaptive controller. Although this method does not present a problem for regulators, it can lead to a steady state error in tracking applications. The sensor blending theory was expanded to take advantage of the system’s dynamics to allow for zero steady state error tracking. This method does not need knowledge of the specific system’s dynamics, but instead uses the structure of the A and B matrices to perform the blending for the general case. The generic adaptive autopilot was tested in two high-fidelity nonlinear simulators of two typical General Aviation aircraft. The results show that the autopilot was able to adapt appropriately to the different aircraft and was able to perform three-dimensional navigation and an ILS approach, without any modification to the controller. The autopilot was tested in moderate atmospheric turbulence, using consumer-grade sensors and actuators currently available in General Aviation aircraft. The generic adaptive autopilot was shown to be robust to atmospheric turbulence and sensor and actuator random noise. In both aircraft simulators, the autopilot adapted successfully to changes in airspeed, altitude, and configuration. This investigation proves the feasibility of a generic autopilot using direct adaptive controller. The autopilot does not need a priori information of the specific aircraft’s dynamics to maintain its safety and stability arguments. Real-time parameter estimation of the aircraft dynamics are not needed. Recommendations for future work are provided.
In this paper, the fault detection and diagnosis problems are considered for a class of discrete nonlinear systems with decentralized event-triggered measurement transmissions. Each sensor determines, according to certain triggering rules, whether to transmit the present measurement to remote filters based on only locally available information. A set of filters is designed where each filter aims to jointly estimate the system states and a specific possible fault. Upper bounds of the estimation error covariances are obtained in the simultaneous presence of the linearization errors and decentralized event-triggered transmissions, and then the filter gains are calculated to minimize such bounds. The filters are designed in a recursive way and thus the algorithm is applicable for online implementation. When a fault is detected, the filter with the least residual is regarded as the one corresponding to the actual fault and its output can be seen as the states and fault estimation. The effectiveness of the proposed method is illustrated by a simulation example.
The self-organizing diagnosis has been studied by applying the idea of autonomous and decentralized systems extracted from the concept of immune network. The model implements network-level recognition by connecting information from local recognition units by dynamical evaluation chain. The model has been further elaborated for engineering concerns of identifying not only sensor faults but process faults. The sensor faults will be identied by evaluating reliability of data from sensor, while the process faults will be identied by evaluating that of constraints that must be satised among these data. We have demonstrated that the extended sensor network will work against both sensor faults and process faults by an illustrative example.